Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

Malignant features of minipig melanomas prior to spontaneous regression

In MeLiM minipigs, melanomas develop around birth, can metastasize, and have histopathologic characteristics similar to humans. Interestingly, MeLiM melanomas eventually regress. This favorable outcome raises the question of their malignancy, which we investigated. We clinically followed tens of tumors from onset to first signs of regression. Transcriptome analysis revealed an enrichment of all cancer hallmarks in melanomas, although no activating or suppressing somatic mutation were found in common driver genes. Analysis of tumor cell genomes revealed high mutation rates without UV signature. Canonical proliferative, survival and angiogenic pathways were detected in MeLiM tumor cells all along progression stages. Functionally, we show that MeLiM melanoma cells are capable to grow in immunocompromised mice, with serial passages and for a longer time than in MeLiM pigs. Pigs set in place an immune response during progression with dense infiltration by myeloid cells while melanoma cells are deficient in B2M expression. To conclude, our data on MeLiM melanomas reveal several malignancy characteristics. The combination of these features with the successful spontaneous regression of these tumors make it an outstanding model to study an efficient anti-tumor immune response.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Cancer drug sensitivity prediction from routine histology images

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomised controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression-based mapping of drug effects on cancer cell lines to train a deep learning model that predicts patients' sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and, ultimately, genetic variation. Mutations generated by APOBEC3 cytidine deaminases can contribute to genetic variation and the consequences of APOBEC activation differ depending on the stage of cancer, with the most significant impact observed during the early stages. However, how APOBEC activity shapes evolutionary patterns of genes in the host genome and differential impacts on cancer-associated and non-cancer genes remain unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer-related genes and controls, suggesting unique associations with cancer. Studying a bat species with many more APOBEC3 genes, we found diverse motif patterns in orthologs of cancer genes compared to controls, similar to humans and suggesting APOBEC evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC-induced heterogeneity enhances cancer evolution, shaping clonal dynamics through bimodal introduction of mutations in certain classes of genes. Our results suggest that a major consequence of the bimodal distribution of APOBEC affects greater cancer heterogeneity.

A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers

Endometrial cancer (EC) is the most frequent gynecologic cancer in postmenopausal women. Pathogenetic mechanisms that are related to the onset and progression of the disease are largely still unknown. A multi-omics strategy can help identify altered pathways that could be targeted for improving therapeutical approaches. In this study we used a multi-omics approach on four EC cell lines for the identification of common dysregulated pathways in type 1 and 2 ECs. We analyzed proteomics and metabolomics of AN3CA, HEC1A, KLE and ISHIKAWA cell lines by mass spectrometry. The bioinformatic analysis identified 22 common pathways that are in common with both types of EC. In addition, we identified five proteins and 13 metabolites common to both types of EC. Western blotting analysis on 10 patients with type 1 and type 2 EC and 10 endometria samples confirmed the altered abundance of NPEPPS. Our multi-omics analysis identified dysregulated proteins and metabolites involved in EC tumor growth. Further studies are needed to understand the role of these molecules in EC. Our data can shed light on common pathways to better understand the mechanisms involved in the development and growth of EC, especially for the development of new therapies.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Hepatogenesis and hepatocarcinogenesis: Alignment of the main signaling pathways

Development is a symphony of cells differentiation in which different signaling pathways are orchestrated at specific times and periods to form mature and functional cells from undifferentiated cells. The similarity of the gene expression profile in malignant and undifferentiated cells is an interesting topic that has been proposed for many years and gave rise to the differentiation-therapy concept, which appears a rational insight and should be reconsidered. Hepatocellular carcinoma (HCC), as the sixth common cancer and the third leading cause of cancer death worldwide, is one of the health-threatening complications in communities where hepatotropic viruses are endemic. Sedentary lifestyle and high intake of calories are other risk factors. HCC is a complex condition in which various dimensions must be addressed, including heterogeneity of cells in the tumor mass, high invasiveness, and underlying diseases that limit the treatment options. Under these restrictions, recognizing, and targeting common signaling pathways during liver development and HCC could expedite to a rational therapeutic approach, reprograming malignant cells to well-differentiated ones in a functional state. Accordingly, in this review, we highlighted the commonalities of signaling pathways in hepatogenesis and hepatocarcinogenesis, and comprised an update on the current status of targeting these pathways in laboratory studies and clinical trials.

Identification of novel key regulatory lncRNAs in gastric adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is one of the most common and deadly cancers worldwide. Recent evidence has demonstrated that dysregulation of long noncoding RNAs (lncRNA) is associated with different hallmarks of cancer. lncRNAs also were suggested as novel promising biomarkers for cancer diagnosis and prognosis. Despite these previous investigations, the expression pattern, diagnostic role, and hallmark association of lncRNAs in STAD remain unclear.

Results

In this study, The STAD lncRNA-mRNA network was constructed based on RNAs that differentially expressed among tumor and normal samples and had a strong expression correlation with others. The high degree nodes of the network were associated with overall survival. In addition, we found that the hubs’ regulatory roles have previously been confirmed in different types of cancers by literature. For example, the HCG22 hub inhibited cell proliferation and invasion and induced apoptosis in oral squamous cell carcinoma (OSCC) cells. The levels of PCNA, Vimentin, and Bcl2 were decreased and E-cadherin and Bax expression was elevated in OSCC cells after HCG22 overexpression. Additionally, HCG22 overexpression inhibited the Akt, mTOR, and Wnt/β-catenin pathways.

Then lncRNAs were mapped to their related GO terms and cancer hallmarks. Based on these mappings, we predict the hallmarks that might be associated with each lncRNA. Finally, the literature review confirmed our prediction.

Among the 20 lncRNAs of the STAD network, 11 lncRNAs (LINC02560, SOX21-AS1, C5orf66-AS1, HCG22, PGM5-AS1, NALT1, ENSG00000241224.2, TINCR, MIR205HG, HNF4A-AS1, ENSG00000262756) demonstrated expression correlation with overall survival (OS). Based on expression analysis, survival analysis, hallmark associations, and literature review, LINC02560, SOX21-AS1, C5orf66-AS1, HCG22, PGM5-AS1, NALT1, ENSG00000241224.2, TINCR, MIR205HG, HNF4A-AS1 plays a regulatory role in STAD. For example, our prediction of association between C5orf66-AS1 expression dysregulation and “sustaining proliferative signal” and “Activating invasion and metastasis” has been confirmed in STAD, OSCC and cervical cancer. Finally, we developed a lncRNA signature with SOX21-AS1 and LINC02560, which classified patients into high and low-risk subgroups with significantly different survival outcomes. The mortality rate of the high-risk patients was significantly higher compared to the low-risk patients (28/1% vs 60.13).

Conclusion

These findings help in designing more precise and detailed experimental studies to find STAD biomarkers and therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08578-6.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Beyond the Usual Suspects: Hepatitis E Virus and Its Implications in Hepatocellular Carcinoma

Hepatitis E virus infections are the leading cause of viral hepatitis in humans, contributing to an estimated 3.3 million symptomatic cases and almost 44,000 deaths annually. Recently, HEV infections have been found to result in chronic liver infection and cirrhosis in severely immunocompromised patients, suggesting the possibility of HEV-induced hepatocarcinogenesis. While HEV-associated formation of HCC has rarely been reported, the expansion of HEV's clinical spectrum and the increasing evidence of chronic HEV infections raise questions about the connection between HEV and HCC. The present review summarizes current clinical evidence of the relationship between HEV and HCC and discusses mechanisms of virus-induced HCC development with regard to HEV pathogenesis. We further elucidate why the development of HEV-induced hepatocellular carcinoma has so rarely been observed and provide an outlook on possible experimental set-ups to study the relationship between HEV and HCC formation.